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Athens Journal of Health - Volume 1, Issue 4 – Pages 271-286

https://doi.org/10.30958/ajh.1-4-3 doi=10.30958/ajh.1-4-3

Patterns of Socio-economic Deprivation and its

Impact on Quality of Life: Case of a Less

Developed Region in West Bengal, India

By Basudatta Sarkar

Haimanti Banerji†

Joy Sen‡

In the current context of regional planning, the issue of socio-

economic deprivation and its impact on quality of life is becoming

highly significant. Though several regions in cities and their fringe

areas are continuously developing, but the community development

blocks of various sub regions in them exhibit a fairly evident pattern

of dichotomy and duality in development. Consequently, the sub

regions become more socio-economically susceptible and more

prone to vulnerability compared to the cities having a higher level of

preparedness in development. The key reason is the unequal

distribution of national assets and resources across the systems of

sub regions. Hence, there is a need to evaluate the degree of

susceptibility in the different sub regions. The susceptibility in the

process of development can be spatially explained by studying the

patterns of deprivation in the sub regions. Additionally there is an

observation on quality of life in terms of poor physical infrastructure

and housing conditions, which has bearing with susceptibility. The

present paper tries to identify the patterns of deprivation and its

impact on quality of life from the two observations. The study has

been performed based on the performances of fifteen socio-economic

growth indicators, broadly categorized as health, education and

economic indicators following Human Development Index (HDI)

guidelines. The patterns of deprivation of a sub region have been

identified by calculating the distribution of deprivation index across

the region. Finally, the paper has tried to understand the nature of

relationship between deprivation index and quality of life indicators.

There is a specific case of Malda, a comparatively less developed

region of West Bengal which has been selected as the case study to

best forward the concerns of the paper.

Senior Research Fellow, Department of Architecture and Planning, Indian Institute of

Technology Kharagpur, India. †Assistant Professor, Department of Architecture and Planning, Indian Institute of Technology

Kharagpur, India. ‡Professor, Department of Architecture and Planning, Indian Institute of Technology

Kharagpur, India.

Vol. 1, No. 4 Sarkar et al.: Patterns of Socio-economic Deprivation…

272

Introduction

Background of the Study

The term deprivation stands for the condition of a system or a community

or a region which is lacking the basic necessities of a society or community.

Analogically, socio-economic deprivation can be described as the lack of social

and economic benefits which are considered to be basic necessities of a society

or community or in a broader sense of a region. The regions with high demand

and low supply of basic requirements often exhibit poor social and economic

status compared to the other adjacent regions which mark the former as socio-

economically deprived region (Pampalon et al., 2000).

According to Maslow‟s hierarchy of needs, the basic necessities refer to

the food, shelter and warmth (Maslow, 1943). The development of any region

primarily depends on the fulfillment of these three prime factors. But

practically, it is difficult to measure the development of any community or

regional system only in terms of availability of food, shelter and warmth. The

fundamental factors have to be more specific and quantified to assess the

degrees of deprivation. Therefore a set of quantitative indicators which

collectively represent the three prime factors of development need to be

identified to measure the overall development of any regional system. In the

present study, to identify the pattern of socio-economic deprivation, the Human

Development Index (HDI) indicators have been considered as the primary units

of measurement. Human Development Index (HDI) is considered worldwide as

a basic yardstick for the measurement of socio-economic development, whose

fulfillment satisfies the reaching of “A composite index measuring average

achievements in three basic dimensions of human development- a long and

healthy life, knowledge and a decent standard of living” (Human Development

Reports, 2003). The performances of HDI based indicators also reflect the

quality of life of people of any particular region. As example, it can be stated

that low per capita income (economic indicator) leads to poor quality of

housing, high illiteracy rate (knowledge indicator) leads to less awareness, less

number of doctors and beds in hospitals (health indicator) leads to poor health

condition etc. Performances of the indicators determine the state of deprivation

and in a larger scale the pattern of deprivation for the whole region.

There are several other vicious causal factors which act upon a region and

make significant diverse changes in the performances of the indicators. The

impact of the factors upon any regional system can be fatal as they expose the

region towards different kinds of social and economic shocks, which in turn

make a socio-economically deprived region highly sensitive. The factors can

be of different types and can emerge from different dimensions. They can

damage in direct and indirect way to both tangible as well as intangible assets

and eventually affect the quality of life of the people living in the affected

region. The extent of damage depends on the nature and intensity of shocks

generated by them (Kim et al., 2009). This paper has primarily referred to a set

of causal factors proposed by different researchers shown in Table 1.

Athens Journal of Health December 2014

273

Table 1. Causal Factors

Source Causal factors

Kelly and

Schmidt, 1995

Poorly developed market and institutional systems, high

dependency rate, and relative unimportance given to

aggregate demand

Loughead et al.,

2001 The limited or no access to the common property

Alcantara Ayala,

2002

Lack of access to resources, disintegration of social patterns,

lack of strong national and local institutional structure, lack of

public awareness, limited access to political power and

representation, and certain beliefs and customs

Ellis F.,

Freeman A. H.,

2004

Unemployment and gender differentiation in work places,

macro-micro linkages, transformation of rural assets into

money, disadvantageous position of women in urban food and

labour market

Sastry, 2004 Poverty, transformation of farm land into urban land, change

in the labour force participation pattern

Briguglio et al,

2006 Opening to the elements of the exogenous shocks

Holmes et al.,

2010

Diversion of mean of earning from agriculture to non-farm

activities, low household income

Pasteur, 2011 Selling assets like land and livestock

Sarkar et al.,

2012

Price change of certain commodity affecting the economy of

people, economic recession, technological changes, a major

change in Government policy decision, a major political

turnover, a change in taxation policy, any new law or

amendment of an existing law

In Indian context, the study on socio-economic deprivation is gradually

becoming significant due to the pressure created over the sub regions by rapid

urbanization, changing pattern of demand and supply, globalization etc which

have created different layers in the process of development. In the present

context the level of development can be explained in three layers of

development- development in cities, development in fringe areas and

development in community development (CD) blocks. The cities are already

developed and resourceful. The fringe areas are trying to be a part of city to

avail all the amenities in full fledged manner and therefore they are gradually

developing. But the area of concern is the development level of CD blocks.

Most of the times, the CD blocks being the most neglected part of a region face

the highest level of deprivation in social as well as economic aspects. This

negligence often leads towards multiple deprivations in both social and

economic dimensions Therefore, in order to understand the pattern of

deprivation, the present study has considered the community development

blocks as the spatial unit forming the sub regions.


274

Need of the Study

It has been established in previous research works, that social and

economic benefits are the basic necessities of a region and lack of the same

makes that particular region exposed to different kinds of shocks and injuries.

The region becomes highly sensitive and susceptible in response to the socio-

economic shocks generated by the causal factors. When the susceptibility of

the sensitive regions exceeds certain limit of tolerance, they become vulnerable

to the similar kind of shocks. Secondly, the quality of life of people living in

CD blocks is of major concern. Poor and degraded quality of life is a very

common phenomenon in those regions. The present study not only attempts to

understand the pattern of socio-economic deprivation in the CD blocks of a

relatively less developed region in West Bengal, but also tries to explore the

existence of any relationship between the pattern of socio-economic

deprivation and poor quality of life. Since, the present study has referred to the

HDI based indicators for measurement of deprivation, the investigation

unveiling the relationship of socio-economic deprivation and poor quality of

life becomes obvious. Accordingly, the following two objectives have been

formulated to accomplish the present study:

- To identify the pattern of socio-economic deprivation

- To understand the relationship of socio-economic deprivation and

poor quality of life

Methods

Case Study Region

To fulfill the objectives, Malda- a relatively less developed region in West

Bengal, located in eastern part of India has been chosen as the case study.

Malda is a district in West Bengal centrally located in the riparian zone of river

Ganges (Figure 1). All CD blocks within it have some commonality in terms of

soil type, spatial characteristics, demographic and socio-economic

characteristics. The region is less addressed and underdeveloped in both

agriculture and industrial sectors and consequently is lacking in basic social

and economic necessities (Shamim and Ahmed, 2011). Low agricultural

productivity, small size of land holding, high dependency on farming, drought

and flood are the additional factors acting behind the state of

underdevelopment (Siddiqui and Hussain, 2010).


275

Figure 1. Location of Case Study Region

Tools and Techniques

For identification of the pattern of deprivation, the standard formula of

indices of multiple deprivations has been used to identify the deprivation index

for each CD block in the case study region. As per the definition given by Peter

Townsend (1987), deprivation can be explained as “a state of observable and

demonstrable disadvantage relative to local community or the wider society or

nation to which the individual, family or group belongs”. Broadly, it can be

classified into two categories- material and social, indicating lack of access to

the basic necessities and social weakness respectively (Pampalon et al., 2012).

The state of deprivation in any region or community can be measured by

deprivation index. Deprivation index is considered as a geographical marker

which indicates the quality of life of people (Pampalon et al., 2009). Multiple

deprivation has been considered as sum total of different dimensions of

deprivation (Noble et al., 2006). In the present study, the dimensions of

deprivation refer to the three basic dimensions of HDI- health, knowledge and

economy. Since, the primary units of study must be as small as possible so that

it can ensure a very high level of accuracy (Pampalon et al., 2000), in this

study, depending upon the availability of data, CD blocks have been

considered as the smallest units. After calculation of the deprivation index a

mapping has been done to visually represent the pattern of deprivation. The

formula for indices of multiple deprivations is as follows:

Iij= (Imax- Ii)/(Imax-Imin)

Iij= Deprivation Index of ith

variable in jth

unit of study

Ii = value of ith

variable in jth

unit of study

Imax= Maximun value of ith

variable

Imin= Minimum value of ith

vatiable

Indices of Multiple Deprivations

DI=ΣIij/n

n= Total number of variables


276

For the second objective, the deprivation indices have been related with

the quality of life indicators and the relationship has been graphically

represented for the detailed illustration. The relationship has been explained

with the help of Pearson‟s correlation coefficient. It is a measure of correlation

between different variables explaining their dependence up on each other. The

correlation coefficient is generally delineated by „r‟. The value of r ranges from

+1 to -1. Variables with the value near to ±1 are considered to have very

strong relationship with each other. The graphical illustrations have been made

based on the z-scores of the QoL indicators and DI of each category of

deprivations.

Results

Selection of Indicators for Deprivation Index

The selection of indicators has emerged as the key concern of the study as

the performances of the indicators are going to determine the degree of

deprivation in every single unit of study. Therefore the indicators have to be

selected with extreme attention so that they can interpret the actual socio-

economic setting of the study region. As the present study from the very

beginning has been emphasizing on the Human Development Index based

indicators to best describe the social and economic scenario of any region, the

three major dimensions have been conceived to frame a guideline for indicator

selection. These three dimensions are- health, knowledge and economy.

For the initial selection of indicators, an exhaustive literature survey has

been carried out from which an inventory of most appropriate indicators has

been made. The inventory has been detailed out in Table 2 where the list of

indicators has been produced along with the sources.

Table 2. Initial List of Indicators Source Indicators

World Bank

Per capita expenditure on health

Under 5 mortality rate

Crude birth rate

Per capita expenditure on education

Literacy rate

Number of school teacher

Expected years of schooling

Enrolment/ number of students

Gross National Income per capita

Per capita income

Below Poverty Line population

Total population

Labour force participation

Telephone subscribers

Gross Domestic Product

Consumption


277

National Sample Survey

Labour force participation rate

Worker population ratio

Proportion unemployment

Unemployment rate

Steinführer et al., 2009

Low income group population

Disabled population

Recent migration

Vulnerable housing

Older population

Eakin et al., 2008

Age

Education level

Adult education level

Number of adults in households

Total area

Livestock

Irrigated area

Tractor

Land rental

Farm tenure

Credit

Insurance

Technical assistance

Climate information centre

Area in crops

Tapsell et al., 2005

Age

Gender

Employment

Occupation

Education level

Household composition

Type of housing

Number of rooms per households

Cutter et al., 2003

Personal wealth

Age

Density of built environment

Housing stock and tenancy

Occupation

Infrastructure dependence

Single sector economic dependence

After the initial compilation, the indicators are fitted into the given three

dimensions. At the same time, three more parameters for selecting the

indicators have been applied to identify the final set. They are: i. Data

availability on local scale, ii. Exclusion of factors that do not affect

vulnerability level within the case study region and iii. No interlink among

factors. The final set of indicators has been enlisted in the Table 3.


278

Table 3. Final Set of Indicators

Health Knowledge Economy

Under five mortality rate Illiteracy rate Unemployment rate

Crude birth rate Teacher in primary school Commercial banks

Beds in hospitals Enrollment in primary

school Rural banks

Polio vaccination No. of primary school Net collection from small

savings

Patients treated Co-operative society

Fair price shop

Pattern of Deprivation

The pattern of deprivation has been identified by calculating the

deprivation index (DI) for each unit of study that is for each CD block of the

case study region. The values of the deprivation index of the CD blocks

represent the spatial distribution of deprivation. To calculate the deprivation

index the data have been collected from the Census of India, 2011

(http://censusindia.gov.in/) and District Statistical Handbook, Malda (2011).

The Census of India is published from the Directorate of Census Operations,

Govt. of India and District Statistical Handbook is published by the Bureau of

Economics and Statistics, Govt. of India. The raw data for each indicator has

been standardized by calculating the z-score. Then the z-score of each indicator

has been put into the formula of indicaes of multiple deprivations. Finally the

average deprivation index for all the CD blocks has been identified. Table 4

shows the deprivation index of each block.

Table 4. Deprivation Index

CD Blocks Average DI

Bamongola 0.878

Old Malda 0.785

Chanchal I 0.768

Ratua II 0.751

Chanchal II 0.748

Harishchandrapur I 0.744

Kaliachak II 0.723

Habibpur 0.683

Harishchandrapur II 0.674

Ratua I 0.634

Kaliachak III 0.577

Manikchak 0.539

Kaliachak I 0.492

Gazole 0.463

English Bazar 0.275


279

Depending upon the values of average DI, the blocks have been

categorized in four groups. They are: very highly deprived (DI>0.8), highly

deprived (0.6>DI>0.79), deprived (0.3>DI>0.59) and less deprived

(0>DI>0.29) region to emphasize the pattern of deprivation. The categorization

in detail shows that there is only one block (Bamongola) which is very highly

deprived, nine blocks are highly deprived, four blocks are deprived and only

one block is less deprived which means a major part of Malda district is highly

deprived in both social and economic dimensions. Only one block is

categorizedas less deprived which is a major issue of concern. The pattern

reveals the existing socio-economic status of the whole case study region that

is the district of Malda. The spatial distribution of deprivation has been shown

in Figure 2.

Figure 2. Pattern of Deprivation

Selection of Quality Of Life Indicators

Quality of life (QoL) is the overall wellbeing of people and society and has

a very wide range of contexts ranging from health to politics, economy to

psychology, education to environment and leisure to social belongings

(Gregory et al., 2009; Nussbaum and Sen, 1993). The World Health

organization (WHO) defines QoL as : “an individual's perception of their

position in life in the context of the culture and value systems in which they

live and in relation to their goals, expectations, standards and concerns. It is a

broad ranging concept affected in a complex way by the person's physical

health, psychological state, personal beliefs, social relationships and their

relationship to salient features of their environment” (http://www.who.int/men

tal_health/media/68.pdf). Therefore it is difficult to identify a particular set of

indicators. Since the present study is more focused on socio-economic

deprivation, authors have considered housing condition and physical

Very highly deprived

Highly deprived

Deprived

Less deprived

http://www.who.int/mental_health/media/68.pdf



280

infrastructural facility indicators as the micro level QoL indicators. The final

set of QoL indicators are enlisted in Table 5.

Table 5. Quality of Life Indicators

Category Indicators

Housing condition Number of households living in temporary housing

Physical infrastructure

Number of households having toilet and bathroom

Number of households having drainage

Number of households having electricity

Number of households having water supply

Impact of Deprivation over Quality of Life

To understand the impact of deprivation over QoL, the correlation

coefficients for DI and all QoL indicators have been determined. The values

explain the very strong relationship between DI and QoL indicators. The

correlation coefficients are shown in Table 6. Total number of households in

study area is 8,46,991 and total population of study area is 39,88,845

(http://censusindia.gov.in/).

Table 6. Correlation of DI and QoL

DI

Number of

households

living in

temporary

housing

Number of

households

having toilet

and bathroom

Number of

households

having drainage

Number of

households

having

electricity

Number of

households

having water

supply

0.979 -0.984 -0.978 -0.992 -0.988

To easily understand the relationship of deprivation with quality of life, a

graphical illustration has been made. Five graphs have been drawn to show the

nature of response of the quality of life indicators to the socio-economic

deprivation. Figure 3(a,b,c,d, and e) illustrates the results. The X-axis

represents the average deprivation index for all four patterns (Figure 2) of

deprivation and Y-axis represents the average z-scores of quality of life

indicators in the four differently deprived regions.

From the figures the observations drawn are:

Figure 3(a): Number of temporary housing increases with high DI indicating

very poor housing conditions in very highly and highly deprived regions.

Figure 3(b): Number of households with toilet and bathroom gradually

decreases with the increase in DI indicating the lack of hygiene, poor health

status and poor condition of living in the very highly and highly deprived

regions.

Figure 3(c): Number of households with drainage facility gradually deceases

with the high DI explaining the poor physical infrastructure leading towards

poor quality of life in the region with high deprivation.


281

Figure 3(d): Number of households with electricity decreases with the increase

in DI indicating the poor infrastructural framework and lack of access to basic

necessities leading towards poor quality of life in the highly deprived regions.

Figure 3(e): Number of households with water supply gradually decreases with

the increase in DI explaining the lack of access to basic necessities, lack of

proper physical infrastructure which leads towards poor living conditions in the

regions with high deprivation.

Figure 3. DI and QoL Relationship

(a)

(b)

(c)

(d)

(a)

(d)


282

(e)

Discussion

The present study has tried to identify the pattern of socio-economic

deprivation in a relatively less developed region and the impact of the socio-

economic deprivation over quality of life of that particular region. Earlier, in

different researches it has been established that, deprivation index is considered

as a widespread tool to understand the pattern and degree of socio-economic

disparities (Drukker et al., 2003; Schuurman et al., 2007). Deprivation deals

with various aspects causing lack of access to basic necessities and related

resources which in turn affects the way of life (Nolan and Marx, 2009;

Townsend 1979). Relevant researches in European countries have established

that socio-economic deprivation and various dimensions of quality of life

especially health related issues are associated with each other (Drukker and Os,

2003; Drukker et al., 2003). A study by Drukker et al. (2003) has revealed the

relationship between socio-economic status and health related quality of life in

The Netherlands. Moreover, the study has shown that, with the little variation

in socio-economic deprivation, there is change in quality of life.

The vast application of the method has been seen mostly in the health

related studies. However in the present paper the authors have tried to apply the

concept of deprivation in case of quality of life of people. Firstly, the results for

pattern of deprivation show a high level of socio-economic deprivation in the

entire Malda district. Also, the quality of life of people living in the case study

region is not up to the mark according to the statistics obtained from different

reliable sources like Census of India 2011 and District Statistical Handbook

2011. Secondly, the results (table 6) explaining the impact of socio-economic

deprivation over quality of life show that with the increased value of

deprivation index, the value of the quality of life indicators decrease. In case of

all the five quality of life indicators namely, number of households living in

temporary housing, number of households with toilet and bathroom, number of

household with drainage, number of household with electricity and number of

household with drinking water supply, have lower values with high deprivation

index. This incident explains that with high deprivation index, the quality of

life becomes poorer, meaning the direct relationship of deprivation and poor

quality of life. Consequently, socio-economically deprived regions always lead

a degraded quality of life in terms of poor housing qualities and poor physical

infrastructure which may lead towards multidimensional vulnerability to

different kinds of social, medical and economic shocks.


283

Conclusions

The preset study has tried to understand the pattern of socio-economic

deprivation in a relatively less developed region of developing nations. The

study shows how the quality of life of people can be affected by deprivation

and how the general idea of quality of life of a particular region can be made

based up on the socio-economic status of the region. The strong level of

association between the DI and QoL explains the appropriateness of the study.

The study identifies the basis of socio-economic susceptibility and consequent

vulnerability by understanding the pattern and relationship of deprivation and

QoL in a developing region. However, deprivation index is not an individual

level measure and also does not provide an descriptive framework. The study is

applicable for any less developed region of developing nations. The indicators

can be modified according to the nature of derivation and focus of the study.

Acknowledgments

Our thanks to Athens Institute for Education and Research for selecting

our paper for the presentation in “4th

Annual International Conference in Urban

Studies and Planning” held in Athens, Greece. The paper is a small part of the

doctoral research pursuing by Basudatta Sarkar. We are also very much

thankful to Indian Institute of Technology Kharagpur, India for providing

financial support to attend the conference and institute research fellowship for

the doctoral research.

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